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Over 80% of all malaria deaths occur in children under 5 years of age. Malaria control
strategies have been progressively shifted to specific populations and/or areas to max-
imize effectiveness. Malaria is a significant public health problem in Ghana. Seasonal
Malaria Chemoprevention (SMC) using a combination of sulfadoxine-pyrimethamine
and amodiaquine has been implemented since 2015 in northern Ghana where malaria
transmission is intense and seasonal. In this study, we estimated the prevalence of
asymptomatic P. falciparum carriers in three ecological zones of Ghana, and com-
pared the sensitivity and specificity of different molecular methods in identifying
asymptomatic infections. Moreover, we examined the frequency of mutations in pfcrt,
pfmdr1, pfdhfr, and pfdhps that relate to the ongoing SMC. A total of 535 asymp-
tomatic schoolchildren were screened by microscopy and PCR (18s rRNA and TARE-
2) methods. Among all samples, 28.6% were detected as positive by 18S nested PCR,
whereas 19.6% were detected by microscopy. A high PCR-based asymptomatic preva-
lence was observed in the north (51%) compared to in the central (27.8%) and south
(16.9%). The prevalence of pfdhfr-N51I/C59R/S108N/pfdhps-A437G quadruple mu-
tant associated with sulfadoxine-pyrimethamine resistance was significantly higher in
the north where SMC was implemented. Compared to 18S rRNA, TARE-2 serves as
a more sensitive molecular marker for detecting submicroscopic asymptomatic infec-
tions in high and low transmission settings. These findings establish a baseline for
monitoring P. falciparum prevalence and resistance in response to SMC over time.
Ghana is also one of the three African countries where the world’s first malaria
vaccine, RTS, S, was launched recently. The vaccine contains part of the central
repeat region and the complete C-terminal of the circumsporozoite protein (CSP)
gene of the 3D7 strain. Polymorphism in the PfCSP protein has been reported from
several parts of the world. However, whether RTS, S-induced immunity is PfCSP
allele-dependent and if selection favors non-3D7 strains are unclear. This study aims
to examine the genetic polymorphism of the PfCSP genes in clinical P. falciparum
cases and provide a baseline of parasite diversity prior to vaccine implementation in
Ghana. A total of 212 clinical samples were collected from Seikwa located in the
Brong Afrong region where the vaccine is currently being deployed. Preliminary data
indicated a high rate of polyclonal infections, with some samples harboring up to 3
clones based on the allele frequency among mapped reads. Parasite clones detected
within the same host were not genetically similar to one another. Instead, they were
distributed in various subclades and closely related to clones identified from other
hosts. It is yet to be investigated if the high PfCSP haplotype diversity and low
resemblance to the 3D7 strain have an impact on the anti-CSP immune response and
thereby the efficacy of RTS,S.
Supply chain disruptions have been a major concern in businesses and society. Our goal is to study these disruptions and analyze ways to mitigate their effects. To do so, we propose an agent-based model simulation. In our model, we consider the following: a supply chain that is generic in the structure; however, we will focus on three types of supply chains, customer-focused, low-cost, and dual-purpose supply chains. Disruptions will include repeatable small-scale events and non-repeatable large events. The managerial decisions considered include excess capacity, the amount of safety stock at each echelon of the supply chain, and the geographical dispersion of partners. To measure the resiliency in our simulation, we will compute the service levels, the time needed to return to normal operations, and the profit.
This dissertation examines whether there are unintended consequences that emerge from status interventions in task groups in relation to cohesion and solidarity. Past theorists have argued that inconsistent status structures produce weaker levels of cohesion and solidarity in
comparison to consistent status structures. To contextualize the issue of group processes as they relate to public policy, I first introduce the complexity framework for public policy and then outline the history of group processes and the concept of solidarity. Despite centuries noting the complexity of group processes and their implications, policymakers remain myopically focused on either individual responsibility or social structure as the root of social inequality. After providing a theoretical overview, I then proceed to discuss the procedure of the study more in-depth. Data come from an online experiment involving mixed-sex dyads interacting in one of three conditions. Participants individually completed an ambiguous problem-solving task and then worked together over Zoom audio to form a group decision. In the three conditions, participants were either given no performance feedback before the problem-solving task or were informed the male or the female participant performed better on a pre-test related to the task. The conversations were recorded and analyzed using measures related to paraverbal synchronization and accommodation. In terms of self-reported cohesion, there appeared to be a difference, albeit a weak one, in only the inconsistent-status condition, with female participants reporting higher levels of cohesion in comparison to males. However, in terms of solidarity, there was no significant difference between the conditions. Although inconsistent status structures were associated with weaker perceptions of cohesion, it did not appear to impact solidarity like theorists have suggested. Status structures do not appear to impact group solidarity. The nature of group membership in conjunction with status consistency/inconsistency may produce the significant differences in solidarity that theorists have suggested. To date, there has been little empirical examination of how status consistency affects cohesion and solidarity. Relatedly, the current study advances the research on vocal accommodation by analyzing status and solidarity simultaneously. The implications of the findings on status interventions for public policy, in particular the nature of feasibility strategies, are discussed in detail at the end.
Studying urban dynamics is essential given the ever-increasing changes in urban areas with all its ensuing consequences, whether negative or positive. It is of paramount importance to take into account the temporal dimension of urban dynamics when studying its patterns and processes. Nevertheless, the majority of studies overlook this consideration and take cross-sectional research approaches. Moreover, a large body of literature in urban dynamics is dedicated to the explanatory analysis and causal inference only, neglecting the importance of predictive analysis. Addressing these two main gaps, this research explores urban dynamics through both causal inference and predictive modeling using longitudinal research designs. Urban dynamics are studied from two aspects in this work; transportation/land-use interactions, and economic growth. In the first article, the impact of built environment on commuting duration is assessed in 2000 and 2015 in Mecklenburg County, NC using spatial panel data models. Results show that the built environment has a statistically significant impact on commuting duration. However, it is important to note that the practical magnitude of the impact is small. In the second and third articles, the business performance of businesses are forecasted for non-business services and business services respectively in Mecklenburg County, NC, using recurrent neural networks long short-term memory deep learning method. After building and training the sequential model, its predictive performance is assessed using out-of-sample evaluation.
The growing complexity of high-performance computing (HPC) systems has led to the development of parallel programming models, such as OpenMP and OpenACC, to make it easier to utilize modern HPC architectures. These models provide a higher-level interface for specifying parallelism patterns and reducing programming effort, but performance optimization and customization are left to the compilers. Despite the availability of state-of-the-art OpenMP compilers, including LLVM, GCC, and ROSE, there remains a need for a compiler that is easily usable and extendable by researchers and students who are not in the field of compiler development, supports multiple parallel programming models, and has comparable performance to mainstream compilers.
The REX compiler has been proposed as a solution to these challenges. It is built upon the ROSE compiler and uses a unified parallel intermediate representation (UPIR), targeting the LLVM OpenMP runtime for optimal performance. REX provides essential OpenMP 5.0/5.1 constructs and preliminary support for OpenACC 3.2. Its source-to-source transformation capabilities offer flexibility and ease of use with minimal overhead. It can be installed as a Docker image or used through a cloud service. The REX compiler's performance has been evaluated using an enhanced version of the parallel benchmark, Rodinia, which compares GPU offloading performance across different parallel programming models and compilers. In conclusion, the REX compiler provides a unique solution for parallel programming research and education, balancing performance, portability, flexibility, and usability.
The increasing prevalence of roads and vehicle traffic, most particularly in urban areas, has a corresponding impact on road mortality, especially for avian species that make use of foraging opportunities along roadside verges. In many cases, raptors, or birds of prey, are vulnerable to vehicle collisions because they forage along roads. The purpose of my research was to conducted a comprehensive investigation into the traffic, habitat and road verge factors that influence collision risk for both nocturnal and diurnal raptors. In addition, I examined the impact that species and individual traits have on the location of vehicle collisions involving birds of prey. I expected to find a notable difference in collision vulnerability between nocturnal and diurnal species. I also expected that road verge vegetation would play a significant role in vehicle collision risk for birds of prey.
Although I did not observe a significant difference in collision risk for raptors based on time of activity, I did find that prey cover in the form of complex vegetation along road verges was an important predictor of collision risk. Dense brush, shrubs or tall grass provide habitat for prey items such as small birds and mammals, which in turn attracts foraging raptors to roadsides, thus increasing the risk of being struck by a passing vehicle.
My analysis of species and individual traits showed that body size and reproductive output were the most important predictors of collision risk. Larger species and those with smaller clutch sizes were most likely to be hit by cars, regardless of road and road verge conditions or habitat characteristics.
Firms have to think creatively and strategically to inform corporate social responsibility that benefits essential stakeholders. Not only is doing good vital for business, but it has become the responsibility of firms to create initiatives that incorporate different stakeholders. Prior research has shown a relationship between CEO leadership styles and CSR initiatives to determine their impact on stakeholders. However, more literature needs to look at different types of leadership styles and different types of CSR focus. This dissertation explores the relationship between executive leadership styles and firm corporate social responsibility engagement with different focuses on philanthropic, operational effectiveness, and business model transformation. It also incorporates the potential moderating effect of CEO narcissism to determine if it amplifies the relationship between a particular leadership style and CSR focus. Stakeholder and Upper echelon theory provide the framework for this study as it explores leadership style and decision-making when leaders consider CSR engagement. This study empirically investigates three leadership styles: servant, transactional, and transformational. The data was collected using a quantitative survey, and the findings provide theoretical and practical insight.
The COVID-19 pandemic has exacerbated unmet mental health needs among adults in the U.S and resulted in significant strains on the U.S. healthcare system. This descriptive, quantitative study aims to investigate reports of unmet mental health needs among adults in the U.S. prior to, and after the onset of the COVID-19 (SARS-CoV-2) pandemic. The purpose of this study is to critically examine state level characteristics and public health response approaches to better understand the contributing factors to mental illness and unmet mental health needs in the U.S. The specific objectives of this study include 1) To create a comprehensive national, longitudinal dataset; 2) To investigate state level variability in regards to mental health outcomes, contrasting states with better and worse mental health indicators; 3) To examine COVID-19 response legislation on mental illness (depression), contrasting states with more restrictive and less restrictive COVID-19 response measures, and 4) To provide an in-depth comparison of the best and worst ranked states.
A major component of this dissertation is the development of a comprehensive state-level dataset that links key state characteristics related to mental illness and COVID-19 response measures to aggregate individual self-report mental health data. The dataset (n=50) consists of 206 total variables sourced from 8 data sources. Descriptive statistics, frequencies, and bivariate analyses were run in SPSS Statistics 28 to determine if there were any correlations among state level characteristics, COVID-19 response measures, and unmet mental health needs. Findings suggest slight correlations among meso- and macrosystem level variables which could be indicative of the impacts of the COVID-19 pandemic on economic and mental health outcomes. Economic characteristics at the macro-system level, such as household income and healthcare spending, look to be associated with better mental health rankings.
This dissertation research provides an original contribution to the field of public health as there is minimal existing literature pertaining to the influence of state level variability on mental illness and unmet mental health needs. This research also provides the groundwork for future studies to build upon the data collected on state level factors which influence mental health outcomes, and to explore the inter-relationships between the U.S. healthcare and economic systems. In terms of health policy, this data and subsequent research will provide guidance for improvements regarding mental health advocacy and reform efforts.
